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Record W4415104427 · doi:10.1021/acs.oprd.5c00262

Development of a Continuous Flow Synthesis of Ezogabine: Process Optimization and Scale-Up

2025· article· en· W4415104427 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganic Process Research & Development · 2025
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsXenon Pharmaceuticals (Canada)BioVectra (Canada)
Fundersnot available
KeywordsContinuous flowFlow chemistryBatch processingProcess developmentContinuous reactorProcess (computing)Process optimizationFlow (mathematics)

Abstract

fetched live from OpenAlex

Ezogabine, a positive allosteric modulator of KCNQ2–5 used to treat epilepsy, was synthesized by a continuous flow hydrogenation process optimized to reduce the overall process and manufacturing mass intensity (PMI/MMI) of the corresponding batch scale procedure. The long reaction time observed in batch mode was streamlined via application of flow chemistry to less than 2 s in 2-MeTHF using Raney-Ni in a Phoenix Flow Reactor system. The continuous flow hydrogenation afforded ezogabine with 99.5% purity by HPLC with significantly reduced PMI (26) and equipment turnover time, thus providing the opportunity for substantial cost reductions relative to batch processing and greater alignment with green chemistry principles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.296
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it